A T–S Fuzzy Model Identification Approach Based on a Modified Inter Type-2 FRCM Algorithm

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Abstract
Hyper-plane-shaped clustering (HPSC) has been demonstrated to be more effective in Takagi-Sugeno (T-S) fuzzy model identification compared to hyper-sphere-shaped clustering. Although some HPSC algorithms, based on type-2 fuzzy theory, have already been developed and have been demonstrated to have outstanding performance in T-S fuzzy modeling, mismatching of the traditional hyper-sphere-shaped membership function and HPSC results will inevitably restrict the modeling performance. In this paper, a modified inter type-2 fuzzy c-regression model (IT2-FCRM) clustering and new hyper-plane-shaped Gaussian membership function were proposed for T-S fuzzy modeling. In the proposed approach, the coefficients of the upper and lower hyperplanes were deduced based on an IT2-FCRM algorithm. Then, a hyper-plane-shaped membership function was directly defined using the hyperplanes to identify the antecedent parameters of the T-S fuzzy model. The experimental results of several benchmark problems show that identification of T-S model accuracy was greatly promoted.
Funding Information
  • National Natural Science Foundation of China (51679095, 51479076)
  • Fundamental Research Funds for the Central Universities (2016YXZD047)

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